from transformers import DebertaV2Tokenizer | |
class DebertaV2JumanppTokenizer(DebertaV2Tokenizer): | |
def __init__(self, *args, **kwargs): | |
super().__init__(*args, **kwargs) | |
self.juman_tokenizer = JumanppTokenizer() | |
def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs) -> tuple[str, dict]: | |
text = self.juman_tokenizer.tokenize(text) | |
add_prefix_space = kwargs.pop("add_prefix_space", False) | |
if is_split_into_words or add_prefix_space: | |
text = " " + text | |
return (text, kwargs) | |
class JumanppTokenizer: | |
def __init__(self): | |
try: | |
import rhoknp | |
except ImportError: | |
raise ImportError( | |
"You need to install rhoknp to use JumanppPreTokenizer. " | |
"See https://github.com/ku-nlp/rhoknp for installation." | |
) | |
self.juman = rhoknp.Jumanpp() | |
def tokenize(self, text: str) -> str: | |
return " ".join([morpheme.surf for morpheme in self.juman.apply_to_sentence(text).morphemes]) | |